Category: Open data

There has recently been a flurry of activity by self-made mappers on the net that major media have noticed. It seems that proliferation of tools such as the excellent TileMill does help to make custom maps a relatively painless, yet still laborious process.

In my experience, a major hurdle in this process is getting good data. Governments and corporations around the globe have made acquiring the goods easier, but the quality frequently leaves one wanting. More about this particular dataset later.

This map is my attempt to visualize real estate prices in Slovenia. Buildings are colored according to the most expensive unit they contain, except in some cases where data is bad. More below.

This dataset is provided by GURS, a government institution. I used it before, to make the map of structure ages in Ljubljana. It comes in a variety of formats, such as SHP (geometry) and text (building properties) files, which were clearly dumped from database tables.

It has some severe problems. For example, some bigger and more expensive buildings contain many units, but these units all hold the same value regardless of their useful area. To make matters more complicated, other multiunit buildings don’t hold the same value for the units they contain. They are, in other words, evidenced correctly. Then, there are building compounds, like the nuclear power plant in Krško, in which every building clearly holds the exorbitant value of entire compound. Some other buildings have price value as zero, and so on.

All of this doesn’t even start to address the quality of valuation the government inspectors performed. In the opinion of many property owners, the values are too low. There’s a new round of valuation coming, in which the values are reportedly bound to drop by further five to twenty percent, if I remember correctly. It will be interesting to make another map with the valuation differences some day.

Massaging the data

This means that the above map is my interpretation of the dataset beyond the visualization itself. In calculating values for visualization, there were several decisions I made:

For multiunit buildings, I calculated the cost of square meter for every unit, then colored the building with color value of the most expensive unit. This was necessary, because some buildings contain many communal areas, garages and parking lots, which are all independently valued. I first tried with a simple average value, but the apartment buildings with many parking boxes and garages were then valued deceivingly low. I tried to make the map more apartment-oriented, so this was a necessary decision to make it more accurately reflect the market.

For incorrectly evidenced buildings with same value (high) unit value, I took the price of one unit, divided by sum of unit areas. I could do this on one unit only, but which one? There’s no easy answer. The average seemed the way to go.

I also made a list of the most expensive buildings by their total Euro value. Individual unit values were summed, except in cases described in the second bullet point above. there I simply took the price of one unit. It’s accessible as a separate vector layer under “Most expensive buildings” menu item.

Findings

Turns out the most expensive buildings are mostly power plants, which is not surprising. In Ljubljana, two of the most expensive buildings were completed recently. Well, the Stožice stadium was not really completed. I don’t know whether it was paid for or not – this is a discourse best suited for political tabloids. See the gallery:

It’s also hardly surprising that the capital and the coast are areas with the most expensive real estate available. The state of city of Maribor is sad to see, though, at least in comparison to Ljubljana.

I suggest taking the tour in the map itself, where I go into a little more depth for some towns and cities. Also, be sure to click the “Most expensive buildings”, then hovering the mouse pointer over highlighted buildings to get an idea of their total cost and price per square meter, which in many cases diverges dramatically.

Here are two charts showing price/m2 distribution at different intervals in time.
This one is an all-time chart. Most buildings are valued low, since all ages were taken into account.

This one shows the period between year 2008 and now, in other words, since the crisis struck. Nevertheless, more expensive buildings seem to prevail. No wonder, since they are new. But that probably also means that there’s more apartment building construction relative to countryside development. I’m not really a real estate expert, so if anyone has a suggestion, comment away.

I also have to thank the kind people at GURS for providing me with data. They know it’s flawed somewhat, but all in all it’s not so bad.

Disclaimer

As I’ve noted before, this map is a result of my interpretation of government data. I’m in no way I responsible for any misunderstandings arising from this map. If you want to see the actual valuation of your building or building unit, please consult GURS or use their web application to find out.

Such is the beauty of open data that when I saw the excellent Portland: The Age of a City by Justin Palmer, I immediately wanted to do something similar, but for my town. The people at the government office (GURS) were kind enough to provide me with the files, and after some coding, here it is.

It’s an exploration of how the city grew through the last century. Blue is old, violet younger, res still younger, bright red the youngest.

Here’s the number of structures built by years. I was able to identify causes for some spikes in building activity, but not all:

1899: four years after the big earthquake,

1919: rebuilding after WW1? I’m not sure there was much destruction here,

1929: more building – in 1929 Ljublaana became the capital of Dravska banovina,

1949: rebuilding after WW2,

1959, 1969, 1979, 1989: might be effects of Yugoslav loans, but I suspect it’s more of an effect of administrative laziness, resulting in entering new buildings into evidence at the end of each decade,

2004: the last surge of prosperity in independent Slovenia.

Generally, it’s been going downhill from 1969 on. The best spots were probably taken by then.

Here’s a animation of the whole thing. It shows city evolution between years 1500 and 2013, since there’s not much happening before that.

Such a young country, but already so messed up. One is inclined to think that all is lost, and one would not be far from the truth. Much ink has already been spilled on sad state of affairs in Slovenia, its fall from grace in European Union, the precipitous decline of living standard of its citizenry and its bleak outlook for the future. Did I mention the rampant corruption of its ruling class and top managers? Best not. This was, after all, supposed to be the next Switzerland.

Blaming the ruling class in mere abstract terms may give one a fleeting satisfaction, but who were the people who led us off the cliff? Someone did govern here, or was at least giving an appearance of governing. Prime ministers are known: Lojze Peterle, Janez Drnovšek, Tone Rop, Andrej Bajuk, Janez Janša, Borut Pahor and currently Alenka Bratušek. These are the main culprits for the downward spiral, of which one can only hope we already passed the first half. Names of their accomplices – the ministers, secretaries, etc. – have a tendency to drift into oblivion, as majority of people preoccupy themselves with the daily grind.

So who were they and how are they connected? Here’s a diagram showing all the government members from 2001 on. I call it “loyalty diagram”, since it was constructed in a way that it shows who is close to whom, and who is hardly loyal to any alliance. The rationale in short is:

Ministers are considered to be very loyal to the prime minister (although I know they are not).

Secretaries a lot less, since they are essentially experts and not politicians.

Secretaries are less loyal to ministers as are ministers to prime minister, but still a lot, since it’s they who appoint them.

Secretaries are loyal to each other, since they are bureaucrats who like their positions and will in theory support each other, although in practice there exist many party rivalries.

Click the link or image below to launch the interactive diagram, which can be searched, panned, and zoomed, and which shows details for every staff member on the government. Red dots are prime ministers, bright blue ministers, dark blue secretaries. Every person is marked with a color of the highest position occupied.

There are a select few of loyal party cadres that every prime minister carries with him, or her, which very rarely, if at all, work with anyone else. These are the dark blue and bright blue dots in close proximity of red dots (prime ministers).

Node radius is proportional to how many times the individual sat in a government over the years. For example, Janez Janša was not only prime minister twice, he also served in other capacities, most notably as Minister of Defense in 1994 and was taking on more and more departmental duties as his government in 2012 slowly disintegrated.

There is a big cluster of common cadres between Janez Drnovšek’s and Anton Rop’s governments. It seems that a lot of secretaries are passed on into the next mandate, except in case of shift between left- and right-wing governments, which perform a purge on inauguration.

Anton Rop had most secretaries and the biggest government. If anything, the governments are getting slimmer with time.

People in the middle of diagram are generally dragged there because of many ties with different prime ministers and ministers, so they are either the most politically promiscuous, or (theoretically) the best experts in their fields, a theory swiftly disproven considering they took on ministerial duties in vastly different departments. These are the most die-hard bureaucrats who mostly didn’t do much else in life except being politicians. For the sake of argument, let’s suppose there are exceptions even between them.

Here is how the social network of government actors evolved over time:

Next diagram shows connections of same cadres to their respective fields of work. Green dots are government offices, other colors are the same as in diagram above. Here one can see, for example:

Who is walking in corridors of true power: prime ministers like to keep close Department of Defence, Department of Finance and Department of Internal Affairs. People close to these offices are the movers and shakers.

How different the governments of Slovenia truly were: departments were clumped together with other departments over time, split and again clumped with other departments. There’s hardly a department which survived this period without being split or clumped, most notably Department of Defense.

Who held which functions, and how are different departments connected with various people.

One Sunday I woke up to incessant and very loud tolling of nearby church bell. It was 9 o’clock in the morning. It didn’t seem fair that an institution can cause so much noise so early. As I work hard during the week, run almost every day, and write software, sometimes until late, I would very much prefer to sleep. The clergy would probably say that honest Christians are already awake at that time, so I’m no good anyway.

I then decided to research the matter. A number of facts surfaced, the most startling of which is a state decree, which states that church bells are not categorized as noise. If an inspector came to my house, measured sound levels while this was going on, and found out that they exceed proscribed levels, he would not be able to fine the aforementioned institution. He would probably bill me for the expenses of his time. But I digress.

Action was taken: city geometry was imported into computer along with church bell coordinates. Aggregate sound pressure for each building was calculated, then ranged so it could be visualized. Additionally, a point where there is least such noise was calculated. You can see results below. The point with least noise is on the green marker in the lower left corner. Lucky owner of that house.

Note: please notify me before embedding this map in your page.

I have to admit that the calculation is naive. It doesn’t take into account the elevation model, neither it accounts for building heights. Sound reflection is also ignored. But my curiosity was satisfied. I do live in the red zone.

Here are same maps on different scales. One is for entire country of Slovenia.

Edit: after this post went viral and other media (Dnevnik.si) published their own versions linking to me, I feel compelled to clarify my position about church bells. Personally, that is, as a person, and not a member of any organization, I’m bothered by long intervals of loud tolling on Sunday mornings. I’m told by other people they don’t like that either, and some other people point out that any attempt at playing music at this volume at similar hour of day would not end well.

I do somewhat like single chimes announcing hours of day, even at night. It’s a part of urban environment, and I’d probably subconsciously miss it should they quit. I’m not against Catholicism, the Church, or faith of any denomination.